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- Emami Khoonsari, Payam, et al.
(författare)
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The human CSF pain proteome
- 2019
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Ingår i: Journal of Proteomics. - : ELSEVIER SCIENCE BV. - 1874-3919 .- 1876-7737. ; 190, s. 67-76
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Tidskriftsartikel (refereegranskat)abstract
- Chronic pain represents one of the major medical challenges in the 21st century, affecting > 1.5 billion of the world population. Overlapping and heterogenous symptoms of various chronic pain conditions complicate their diagnosis, emphasizing the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms. We have here investigated proteins found in human CSF with respect to known "pain" genes and in a cohort of patients with dysfunctional pain (fibromyalgia, FM), inflammatory pain (rheumatoid arthritis patients, RA) and non-pain controls utilized semi-quantitative proteomics using mass spectrometry (MS) to explore quantitative differences between these cohorts of patients. We found that "pain proteins" detected in CSF using MS are typically related to synaptic transmission, inflammatory responses, neuropeptide signaling- and hormonal activity. In addition, we found ten proteins potentially associated with chronic pain in FM and RA: neural cell adhesion molecule L1, complement C4-A, lysozyme C, receptor-type tyrosine-protein phosphatase zeta, apolipoprotein D, alpha-1-antichymotrypsin, granulins, calcium/calmodulin-dependent protein kinase type II subunit alpha, mast/stem cell growth factor receptor Kit, prolow-density lipoprotein receptor-related protein 1. These proteins might be of importance for understanding the mechanisms of dysfunctional/inflammatory chronic pain and also for use as potential biomarkers. Significance: Chronic pain is a common disease and it poses a large burden on worldwide health. Fibromyalgia (FM) is a heterogeneous disease of unknown etiology characterized by chronic widespread pain (CWP). The diagnosis and treatment of FM is based on the analysis of clinical assessments and no measurable biomarkers are available. Cerebrospinal fluid (CSF) has been historically considered as a rich source of biomarkers for diseases of nervous system including chronic pain. Here, we explore CSF proteome of FM patients utilizing mass spectrometry based quantitative proteomics method combined with multivariate data analysis in order to monitor the dynamics of the CSF proteome. Our findings in this exploratory study support notable presence of pain related proteins in CSF yet with specific domains including inflammatory responses, neuropeptide signaling- and hormonal activity. We have investigated molecular functions of significantly altered proteins and demonstrate presence of 176 known pain related proteins in CSF. In addition, we found ten proteins potentially associated with pain in FM and RA: neural cell adhesion molecule L1, complement C4-A, lysozyme C, receptor-type tyrosine-protein phosphatase zeta, apolipoprotein D, alpha-1-antichymotrypsin, granulins, calcium/calmodulindependent protein kinase type II subunit alpha, mast/stem cell growth factor receptor Kit, prolow-density lipoprotein receptor-related protein 1. These proteins are novel in the context of FM but are known to be involved in pain mechanisms including inflammatory response and signal transduction. These results should be of clear significance and interest for researchers and clinicians working in the field of pain utilizing human CSF and MS based proteomics.
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| 3. |
- Spjuth, Ola, Professor, 1977-, et al.
(författare)
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Approaches for containerized scientific workflows in cloud environments with applications in life science
- 2021
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Ingår i: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 10, s. 513-513
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this article, we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.
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